Agent activity, source quality, and human review states were hard to read in one place.
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Case study 01 / AI agent platform
Scope turns agent operations into a clear command center.
A management system for AI agents, search workflows, and operational knowledge, designed for teams that need visibility, control, and confidence.
Overview
A compact operating layer for teams managing AI agents at scale.
AI operations leads, knowledge teams, support managers, and domain reviewers.
Product strategy, information architecture, workflow design, and visual system.
A command center that makes agents, evidence, status, and handoffs inspectable.
Challenge
The product had to make AI behavior feel auditable without slowing operators down.
The core UX challenge was reducing ambiguity. Operators needed to understand what an agent did, which sources shaped the answer, where confidence was low, and when a human needed to step in.
I treated the interface as an operational cockpit: dense enough for advanced work, but organized around a few repeatable patterns so status, evidence, and actions stayed predictable.
A dense dashboard that still reads as a command center.
The main view brings volume, transfer reasons, RMA signals, and agent outcomes into one operating surface so teams can spot patterns before opening a detailed review.
Flow in motion
Interaction passes showing how operators move from signal to evidence.
Dashboard interaction
Keep dense operational signals inspectable.
The flow shows how status, charts, and issue evidence stay close enough for operators to investigate without losing the dashboard context.
Evidence review
Let support and ops teams inspect the why behind a signal.
The side-panel flow keeps root causes, issue notes, and trend context attached to the dashboard instead of forcing a separate investigation path.
Process
From agent lifecycle mapping to a scalable review system.
01 / Research
Mapped agent states, failed searches, reviewer needs, and high-risk handoff moments.
02 / IA
Grouped agents, queries, sources, confidence, and review actions into a clear hierarchy.
03 / Flows
Designed paths for search review, escalation, evidence inspection, and agent monitoring.
04 / System
Created reusable status, confidence, evidence, and activity components.
05 / Prototype
Tested progressive disclosure and hover states for dense information without visual noise.
Confidence as a system
Used confidence, source quality, and review state as first-class UI signals instead of hidden metadata.
Evidence before action
Placed source trails next to decisions so operators could validate results before approving them.
Escalation paths
Designed clear ownership and handoff states for moments where automation needed human judgment.
Final experience
Key surfaces to show the product thinking, not only the UI craft.
01 / Issue intelligence
Make exceptions visible before they become escalations.
RMA trends, issue summaries, and escalation evidence sit beside the dashboard so teams can understand where automation needs human support.
02 / Evidence review
Review the original conversation without leaving the operating view.
A focused conversation preview lets operators validate the customer context before changing status, routing ownership, or approving the next action.
03 / Conversation signals
Turn conversation quality into readable signals.
Expression scores, sentiment breakdowns, and service markers help reviewers see patterns across user and assistant turns without reading every message first.
04 / Agent workspace
Give operators controls without breaking their flow.
The chat workspace keeps response shortcuts, source options, prompt settings, and conversation history visible enough for fast decisions and controlled handoffs.
Impact
A clearer way to operate AI systems with human accountability.
Reduced uncertainty around agent behavior by making status, confidence, and source trails visible.
Created a repeatable design language for complex AI operations and review workflows.
Made the product easier to explain to technical, operational, and leadership stakeholders.
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